diff --git a/src/chat/emoji_system/emoji_manager.py b/src/chat/emoji_system/emoji_manager.py index b3c2493d3..918b83969 100644 --- a/src/chat/emoji_system/emoji_manager.py +++ b/src/chat/emoji_system/emoji_manager.py @@ -525,9 +525,9 @@ class EmojiManager: 如果文件已被删除，则执行对象的删除方法并从列表中移除 """ try: - if not self.emoji_objects: - logger.warning("[检查] emoji_objects为空，跳过完整性检查") - return + # if not self.emoji_objects: + # logger.warning("[检查] emoji_objects为空，跳过完整性检查") + # return total_count = len(self.emoji_objects) self.emoji_num = total_count @@ -707,6 +707,38 @@ class EmojiManager: return emoji return None # 如果循环结束还没找到，则返回 None + async def get_emoji_description_by_hash(self, emoji_hash: str) -> Optional[str]: + """根据哈希值获取已注册表情包的描述 + + Args: + emoji_hash: 表情包的哈希值 + + Returns: + Optional[str]: 表情包描述，如果未找到则返回None + """ + try: + # 先从内存中查找 + emoji = await self.get_emoji_from_manager(emoji_hash) + if emoji and emoji.description: + logger.info(f"[缓存命中] 从内存获取表情包描述: {emoji.description[:50]}...") + return emoji.description + + # 如果内存中没有，从数据库查找 + self._ensure_db() + try: + emoji_record = Emoji.get_or_none(Emoji.emoji_hash == emoji_hash) + if emoji_record and emoji_record.description: + logger.info(f"[缓存命中] 从数据库获取表情包描述: {emoji_record.description[:50]}...") + return emoji_record.description + except Exception as e: + logger.error(f"从数据库查询表情包描述时出错: {e}") + + return None + + except Exception as e: + logger.error(f"获取表情包描述失败 (Hash: {emoji_hash}): {str(e)}") + return None + async def delete_emoji(self, emoji_hash: str) -> bool: """根据哈希值删除表情包 diff --git a/src/chat/memory_system/Hippocampus.py b/src/chat/memory_system/Hippocampus.py index 13cf53f22..7446f71ea 100644 --- a/src/chat/memory_system/Hippocampus.py +++ b/src/chat/memory_system/Hippocampus.py @@ -324,7 +324,8 @@ class Hippocampus: words = jieba.cut(text) keywords = [word for word in words if len(word) > 1] keywords = list(set(keywords))[:3] # 限制最多3个关键词 - logger.info(f"提取关键词: {keywords}") + if keywords: + logger.info(f"提取关键词: {keywords}") return keywords elif text_length <= 10: topic_num = [1,3] # 6-10字符: 1个关键词 (27.18%的文本) @@ -353,7 +354,8 @@ class Hippocampus: if keyword.strip() ] - logger.info(f"提取关键词: {keywords}") + if keywords: + logger.info(f"提取关键词: {keywords}") return keywords diff --git a/src/chat/utils/utils_image.py b/src/chat/utils/utils_image.py index 858d95aa3..7f14aa6d4 100644 --- a/src/chat/utils/utils_image.py +++ b/src/chat/utils/utils_image.py @@ -37,7 +37,7 @@ class ImageManager: self._ensure_image_dir() self._initialized = True - self._llm = LLMRequest(model=global_config.model.vlm, temperature=0.4, max_tokens=300, request_type="image") + self.vlm = LLMRequest(model=global_config.model.vlm, temperature=0.4, max_tokens=300, request_type="image") try: db.connect(reuse_if_open=True) @@ -94,7 +94,7 @@ class ImageManager: logger.error(f"保存描述到数据库失败 (Peewee): {str(e)}") async def get_emoji_description(self, image_base64: str) -> str: - """获取表情包描述，使用二步走识别并带缓存优化""" + """获取表情包描述，优先使用Emoji表中的缓存数据""" try: # 计算图片哈希 # 确保base64字符串只包含ASCII字符 @@ -104,9 +104,21 @@ class ImageManager: image_hash = hashlib.md5(image_bytes).hexdigest() image_format = Image.open(io.BytesIO(image_bytes)).format.lower() # type: ignore - # 查询缓存的描述 + # 优先使用EmojiManager查询已注册表情包的描述 + try: + from src.chat.emoji_system.emoji_manager import get_emoji_manager + emoji_manager = get_emoji_manager() + cached_emoji_description = await emoji_manager.get_emoji_description_by_hash(image_hash) + if cached_emoji_description: + logger.info(f"[缓存命中] 使用已注册表情包描述: {cached_emoji_description[:50]}...") + return cached_emoji_description + except Exception as e: + logger.debug(f"查询EmojiManager时出错: {e}") + + # 查询ImageDescriptions表的缓存描述 cached_description = self._get_description_from_db(image_hash, "emoji") if cached_description: + logger.info(f"[缓存命中] 使用ImageDescriptions表中的描述: {cached_description[:50]}...") return f"[表情包：{cached_description}]" # === 二步走识别流程 === @@ -118,10 +130,10 @@ class ImageManager: logger.warning("GIF转换失败，无法获取描述") return "[表情包(GIF处理失败)]" vlm_prompt = "这是一个动态图表情包，每一张图代表了动态图的某一帧，黑色背景代表透明，描述一下表情包表达的情感和内容，描述细节，从互联网梗,meme的角度去分析" - detailed_description, _ = await self._llm.generate_response_for_image(vlm_prompt, image_base64_processed, "jpg") + detailed_description, _ = await self.vlm.generate_response_for_image(vlm_prompt, image_base64_processed, "jpg") else: vlm_prompt = "这是一个表情包，请详细描述一下表情包所表达的情感和内容，描述细节，从互联网梗,meme的角度去分析" - detailed_description, _ = await self._llm.generate_response_for_image(vlm_prompt, image_base64, image_format) + detailed_description, _ = await self.vlm.generate_response_for_image(vlm_prompt, image_base64, image_format) if detailed_description is None: logger.warning("VLM未能生成表情包详细描述") @@ -158,7 +170,7 @@ class ImageManager: if len(emotions) > 1 and emotions[1] != emotions[0]: final_emotion = f"{emotions[0]}，{emotions[1]}" - logger.info(f"[二步走识别] 详细描述: {detailed_description[:50]}... -> 情感标签: {final_emotion}") + logger.info(f"[emoji识别] 详细描述: {detailed_description[:50]}... -> 情感标签: {final_emotion}") # 再次检查缓存，防止并发写入时重复生成 cached_description = self._get_description_from_db(image_hash, "emoji") @@ -201,13 +213,13 @@ class ImageManager: self._save_description_to_db(image_hash, final_emotion, "emoji") return f"[表情包：{final_emotion}]" - + except Exception as e: logger.error(f"获取表情包描述失败: {str(e)}") - return "[表情包]" + return "[表情包(处理失败)]" async def get_image_description(self, image_base64: str) -> str: - """获取普通图片描述，带查重和保存功能""" + """获取普通图片描述，优先使用Images表中的缓存数据""" try: # 计算图片哈希 if isinstance(image_base64, str): @@ -215,7 +227,7 @@ class ImageManager: image_bytes = base64.b64decode(image_base64) image_hash = hashlib.md5(image_bytes).hexdigest() - # 检查图片是否已存在 + # 优先检查Images表中是否已有完整的描述 existing_image = Images.get_or_none(Images.emoji_hash == image_hash) if existing_image: # 更新计数 @@ -227,18 +239,20 @@ class ImageManager: # 如果已有描述，直接返回 if existing_image.description: + logger.debug(f"[缓存命中] 使用Images表中的图片描述: {existing_image.description[:50]}...") return f"[图片：{existing_image.description}]" - # 查询缓存的描述 + # 查询ImageDescriptions表的缓存描述 cached_description = self._get_description_from_db(image_hash, "image") if cached_description: - logger.debug(f"图片描述缓存中 {cached_description}") + logger.debug(f"[缓存命中] 使用ImageDescriptions表中的描述: {cached_description[:50]}...") return f"[图片：{cached_description}]" # 调用AI获取描述 image_format = Image.open(io.BytesIO(image_bytes)).format.lower() # type: ignore prompt = global_config.custom_prompt.image_prompt - description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format) + logger.info(f"[VLM调用] 为图片生成新描述 (Hash: {image_hash[:8]}...)") + description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format) if description is None: logger.warning("AI未能生成图片描述") @@ -266,6 +280,7 @@ class ImageManager: if not hasattr(existing_image, "vlm_processed") or existing_image.vlm_processed is None: existing_image.vlm_processed = True existing_image.save() + logger.debug(f"[数据库] 更新已有图片记录: {image_hash[:8]}...") else: Images.create( image_id=str(uuid.uuid4()), @@ -277,16 +292,18 @@ class ImageManager: vlm_processed=True, count=1, ) + logger.debug(f"[数据库] 创建新图片记录: {image_hash[:8]}...") except Exception as e: logger.error(f"保存图片文件或元数据失败: {str(e)}") - # 保存描述到ImageDescriptions表 + # 保存描述到ImageDescriptions表作为备用缓存 self._save_description_to_db(image_hash, description, "image") + logger.info(f"[VLM完成] 图片描述生成: {description[:50]}...") return f"[图片：{description}]" except Exception as e: logger.error(f"获取图片描述失败: {str(e)}") - return "[图片]" + return "[图片(处理失败)]" @staticmethod def transform_gif(gif_base64: str, similarity_threshold: float = 1000.0, max_frames: int = 15) -> Optional[str]: @@ -502,12 +519,28 @@ class ImageManager: image_bytes = base64.b64decode(image_base64) image_hash = hashlib.md5(image_bytes).hexdigest() - # 先检查缓存的描述 + # 获取当前图片记录 + image = Images.get(Images.image_id == image_id) + + # 优先检查是否已有其他相同哈希的图片记录包含描述 + existing_with_description = Images.get_or_none( + (Images.emoji_hash == image_hash) & + (Images.description.is_null(False)) & + (Images.description != "") + ) + if existing_with_description and existing_with_description.id != image.id: + logger.debug(f"[缓存复用] 从其他相同图片记录复用描述: {existing_with_description.description[:50]}...") + image.description = existing_with_description.description + image.vlm_processed = True + image.save() + # 同时保存到ImageDescriptions表作为备用缓存 + self._save_description_to_db(image_hash, existing_with_description.description, "image") + return + + # 检查ImageDescriptions表的缓存描述 cached_description = self._get_description_from_db(image_hash, "image") if cached_description: - logger.debug(f"VLM处理时发现缓存描述: {cached_description}") - # 更新数据库 - image = Images.get(Images.image_id == image_id) + logger.debug(f"[缓存复用] 从ImageDescriptions表复用描述: {cached_description[:50]}...") image.description = cached_description image.vlm_processed = True image.save() @@ -520,7 +553,8 @@ class ImageManager: prompt = global_config.custom_prompt.image_prompt # 获取VLM描述 - description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format) + logger.info(f"[VLM异步调用] 为图片生成描述 (ID: {image_id}, Hash: {image_hash[:8]}...)") + description, _ = await self.vlm.generate_response_for_image(prompt, image_base64, image_format) if description is None: logger.warning("VLM未能生成图片描述") @@ -533,14 +567,15 @@ class ImageManager: description = cached_description # 更新数据库 - image = Images.get(Images.image_id == image_id) image.description = description image.vlm_processed = True image.save() - # 保存描述到ImageDescriptions表 + # 保存描述到ImageDescriptions表作为备用缓存 self._save_description_to_db(image_hash, description, "image") + logger.info(f"[VLM异步完成] 图片描述生成: {description[:50]}...") + except Exception as e: logger.error(f"VLM处理图片失败: {str(e)}") diff --git a/src/plugin_system/apis/send_api.py b/src/plugin_system/apis/send_api.py index f7b3092ef..a82112931 100644 --- a/src/plugin_system/apis/send_api.py +++ b/src/plugin_system/apis/send_api.py @@ -19,11 +19,9 @@ await send_api.custom_message("video", video_data, "123456", True) """ -import asyncio import traceback import time import difflib -import re from typing import Optional, Union from src.common.logger import get_logger